Human-driven design-to-cost methodology for industrial cost optimization

Over the years cost optimization has gained a strategic importance to realize competitive products. However, traditional approaches are no longer efficient in modern highly competitive industrial scenarios, where numerous factors have to be contemporarily considered and optimized. In order to be effective, design has to care about cost along all its phases. This paper presents a methodology that integrates Design-To-Cost (DTC), Design for Manufacturing and Assembly (DFMA), Human Factors (HF) and Feature-Based Costing (FBC) to include costs from the early conceptual design stages and properly drive the product design. Thanks to a structured knowledge base and a FBC approach, it predicts both manufacturing and assembly processes from the 3D geometrical models and estimate the global costs, more accurately than existing tools. The research demonstrates the method validity by an industrial case study focusing on cost optimization of packaging machines. Thanks to the proposed method, the main design inefficiencies are easily identified from the early design stages and optimization actions are taken in advanced, in respect to traditional design process. Such actions allowed reducing total industrial costs of 20%, improving machine assemblability and human ergonomics due to structure simplification, part number reduction, and production processes modification, and reducing the time spent for cost estimation (until -60%).